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Deep Learning: Deep Learning and Vascular Disease Imaging Pearls - Educational Tools | CT Scanning | CT Imaging | CT Scan Protocols - CTisus
Imaging Pearls ❯ Deep Learning ❯ Deep Learning and Vascular Disease

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  • This study showed that a radiomics-based model can achieve equivalent performance as an experienced academic radiologist in the classification of a wide array of pancreatic cysts with variable malignant potential. This model has the potential to refine pancreatic cyst management by improving diagnostic accuracy of cystic lesions, which can minimize healthcare utilization while maximizing detection of malignant lesions. This study confirms the ability of a radiomic based model to accurately classify pancreatic cystic neoplasms. Further validation and clinical integration of this model could help optimize management of pancreatic cysts by maximizing the rate of detection of malignant lesions while reducing healthcare utilization.
    Classification of pancreatic cystic neoplasms using radiomic feature analysis is equivalent to an experienced academic radiologist: a step toward computer‑augmented diagnostics for radiologists
    Linda C. Chu et al.
    Abdominal Radiology (2022) 47:4139–4150
  • BACKGROUND. CT-based body composition (BC) measurements have historically been too resource intensive to analyze for widespread use and have lacked robust comparison with traditional weight metrics for predicting cardiovascular risk.
    OBJECTIVE. The aim of this study was to determine whether BC measurements obtained from routine CT scans by use of a fully automated deep learning algorithm could predict subsequent cardiovascular events independently from weight, BMI, and additional cardiovascular risk factors.
    CONCLUSION. VFA derived from fully automated and normalized analysis of abdominal CT examinations predicts subsequent MI or stroke in Black and White patients, independent of traditional weight metrics, and should be considered an adjunct to BMI in risk models.
    CLINICAL IMPACT. Fully automated and normalized BC analysis of abdominal CT has promise to augment traditional cardiovascular risk prediction models.
    Utility of Normalized Body Composition Areas, Derived From Outpatient Abdominal CT Using a Fully Automated Deep Learning Method, for Predicting Subsequent Cardiovascular Events
    Kirti Magudia,et al.
    AJR 2023; 220:1–9
  • METHODS. This retrospective study included 9752 outpatients (5519 women and4233 men; mean age, 53.2 years; 890 patients self-reported their race as Black and 8862single health system from January 2012 through December 2012 and who were given no major cardiovascular or oncologic diagnosis within 3 months of undergoing CT. Using fully automated deep learning BC analysis was performed at the L3 vertebral body level to determine three BC areas (skeletal muscle area [SMA], visceral fat area [VFA], and subcutaneous fat area [SFA]). Age-, sex-, and race-normalized reference curves were used to generate z scores for the three BC areas. Subsequent myocardial infarction (MI) or stroke was determined from the electronic medical record. Multivariable- adjusted Cox proportional hazards models were used to determine hazard ratios (HRs) for MI or stroke within 5 years after CT for the three BC area z scores, with adjustment for normalized weight, normalized BMI, and additional cardiovascular risk factors smoking status, diabetes diagnosis, and systolic blood pressure).
    Utility of Normalized Body Composition Areas, Derived From Outpatient Abdominal CT Using a Fully Automated Deep Learning Method, for Predicting Subsequent Cardiovascular Events
    Kirti Magudia,et al.
    AJR 2023; 220:1–9
  • Key Finding 
    - After normalization for age, sex, and race, VFA from routine CT was associated with risk of MI and stroke (HR, 1.31 [95% CI, 1.03–1.67] and 1.46 [95% CI, 1.07–2.00], both p = .04 for overall effect) in multivariable models in Black and White patients; weight, BMI, SMA, and SFA were not. Importance
    - VFA from automated CT analysis predicts MI and stroke, independent of traditional weight metrics, and may serve as an adjunct to BMI in risk models.  
    Utility of Normalized Body Composition Areas, Derived From Outpatient Abdominal CT Using a Fully Automated Deep Learning Method, for Predicting Subsequent Cardiovascular Events
    Kirti Magudia,et al.
    AJR 2023; 220:1–9
  • PURPOSE: The purpose of this study was to evaluate the impact of artificial intelligence (AI)-based noise reduction algorithm on aorta computed tomography angiography (CTA) image quality (IQ) at 80 kVp tube voltage and 40 mL contrast medium (CM).
    CONCLUSIONS: The AI-based noise reduction could improve the IQ of aorta CTA with low kV and reduced CM, which achieved the potential of radiation dose and contrast media reduction compared with conventional aorta CTA protocol.
    Application of Artificial Intelligence-based Image Optimization for Computed Tomography Angiography of the Aorta With Low Tube Voltage and Reduced Contrast Medium Volume.
    Wang Y et al.
    J Thorac Imaging. 2019 Nov;34(6):393-399.
  • “Computerized Tomography Angiography (CTA) based follow-up of Abdominal Aortic Aneurysms (AAA) treated with Endovascular Aneurysm Repair (EVAR) is essential to evaluate the progress of the patient and detect complications. In this context, accurate quantification of post-operative thrombus volume is required. However, a proper evaluation is hindered by the lack of automatic, robust and reproducible thrombus segmentation algorithms. We propose a new fully automatic approach based on Deep Convolutional Neural Networks (DCNN) for robust and reproducible thrombus region of interest detection and subsequent fine thrombus segmentation.”
    Fully automatic detection and segmentation of abdominal aortic thrombus in post-operative CT images using Deep Convolutional Neural Networks.
    López-Linares K et al.
    Med Image Anal. 2018 May;46:202-214. 
  • "The DetecNet detection network is adapted to perform region of interest extraction from a complete CTA and a new segmentation network architecture, based on Fully Convolutional Networks and a Holistically-Nested Edge Detection Network, is presented. These networks are trained, validated and tested in 13 post-operative CTA volumes of different patients using a 4-fold cross-validation approach to provide more robustness to the results. Our pipeline achieves a Dice score of more than 82% for post-operative thrombus segmentation and provides a mean relative volume difference between ground truth and automatic segmentation that lays within the experienced human observer variance without the need of human intervention in most common cases."
    Fully automatic detection and segmentation of abdominal aortic thrombus in post-operative CT images using Deep Convolutional Neural Networks.
    López-Linares K et al.
    Med Image Anal. 2018 May;46:202-214. 
  • Purpose: To evaluate the opinion and assessment of radiologists, surgeons and medical students on a number of important topics regarding the future of radiology, such as artificial intelligence (AI), turf battles, teleradiology and 3D-printing.
    Conclusions: With regard to AI, radiologists expect their workflow to become more efficient and tend to support the use of AI, whereas medical students and surgeons tend to be more skeptical towards this technology. Medical students see AI as a potential threat to diagnostic radiologists, while radiologists themselves are rather afraid of turf losses.
    A survey on the future of radiology among radiologists, medical students T and surgeons: Students and surgeons tend to be more skeptical about artificial intelligence and radiologists may fear that other disciplines take over
    Jasper van Hoek et al.
    European Journal of Radiology 121 (2019) 108742

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